docs: def _minimize_neldermead(
      func,
      x0,
      args=(),
      callback=None,
      xtol=1e-4,
      ftol=1e-4,
      maxiter=None,
      maxfev=None,
      disp=False,
      return_all=False,
  ):  # pragma: no cover
      """
      Minimization of scalar function of one or more variables using the
      Nelder-Mead algorithm.
  
      Options for the Nelder-Mead algorithm are:
          disp : bool
              Set to True to print convergence messages.
          xtol : float
              Relative error in solution `xopt` acceptable for convergence.
          ftol : float
              Relative error in ``fun(xopt)`` acceptable for convergence.
          maxiter : int
              Maximum number of iterations to perform.
          maxfev : int
              Maximum number of function evaluations to make.
      """
      maxfun = maxfev
      retall = return_all
  
      fcalls, func = wrap_function(func, args)
      x0 = asfarray(x0).flatten()
      N = len(x0)
      rank = len(x0.shape)
      if not -1 < rank < 2:
          raise ValueError("Initial guess must be a scalar or rank-1 sequence.")
      if maxiter is None:
          maxiter = N * 200
      if maxfun is None:
          maxfun = N * 200
  
      rho = 1
      chi = 2
      psi = 0.5
      sigma = 0.5
      one2np1 = list(range(1, N + 1))
  
      if rank == 0:
          sim = numpy.zeros((N + 1,), dtype=x0.dtype)
      else:
          sim = numpy.zeros((N + 1, N), dtype=x0.dtype)
      fsim = numpy.zeros((N + 1,), float)
      sim[0] = x0
      if retall:
          allvecs = [sim[0]]
      fsim[0] = func(x0)
      nonzdelt = 0.05
      zdelt = 0.00025
      for k in range(0, N):
          y = numpy.array(x0, copy=True)
          if y[k] != 0:
              y[k] = (1 + nonzdelt) * y[k]
          else:
              y[k] = zdelt
  
          sim[k + 1] = y
          f = func(y)
          fsim[k + 1] = f
  
      ind = numpy.argsort(fsim)
      fsim = numpy.take(fsim, ind, 0)
      # sort so sim[0,:] has the lowest function value
      sim = numpy.take(sim, ind, 0)
  
      iterations = 1
  
      while fcalls[0] < maxfun and iterations < maxiter:
          if (
              numpy.max(numpy.ravel(numpy.abs((sim[1:] - sim[0]) / sim[0])))
              <= xtol
              and numpy.max(numpy.abs((fsim[0] - fsim[1:]) / fsim[0])) <= ftol
          ):
              break
  
          xbar = numpy.add.reduce(sim[:-1], 0) / N
          xr = (1 + rho) * xbar - rho * sim[-1]
          fxr = func(xr)
          doshrink = 0
  
          if fxr < fsim[0]:
              xe = (1 + rho * chi) * xbar - rho * chi * sim[-1]
              fxe = func(xe)
  
              if fxe < fxr:
                  sim[-1] = xe
                  fsim[-1] = fxe
              else:
                  sim[-1] = xr
                  fsim[-1] = fxr
          else:  # fsim[0] <= fxr
              if fxr < fsim[-2]:
                  sim[-1] = xr
                  fsim[-1] = fxr
              else:  # fxr >= fsim[-2]
                  # Perform contraction
                  if fxr < fsim[-1]:
                      xc = (1 + psi * rho) * xbar - psi * rho * sim[-1]
                      fxc = func(xc)
  
                      if fxc <= fxr:
                          sim[-1] = xc
                          fsim[-1] = fxc
                      else:
                          doshrink = 1
                  else:
                      # Perform an inside contraction
                      xcc = (1 - psi) * xbar + psi * sim[-1]
                      fxcc = func(xcc)
  
                      if fxcc < fsim[-1]:
                          sim[-1] = xcc
                          fsim[-1] = fxcc
                      else:
                          doshrink = 1
  
                  if doshrink:
                      for j in one2np1:
                          sim[j] = sim[0] + sigma * (sim[j] - sim[0])
                          fsim[j] = func(sim[j])
  
          ind = numpy.argsort(fsim)
          sim = numpy.take(sim, ind, 0)
          fsim = numpy.take(fsim, ind, 0)
          if callback is not None:
              callback(sim[0])
          iterations += 1
          if retall:
              allvecs.append(sim[0])
  
      x = sim[0]
      fval = numpy.min(fsim)
      warnflag = 0
  
      if fcalls[0] >= maxfun:
          warnflag = 1
          msg = _status_message["maxfev"]
          if disp:
              print("Warning: " + msg)
      elif iterations >= maxiter:
          warnflag = 2
          msg = _status_message["maxiter"]
          if disp:
              print("Warning: " + msg)
      else:
          msg = _status_message["success"]
          if disp:
              print(msg)
              print("         Current function value: %f" % fval)
              print("         Iterations: %d" % iterations)
              print("         Function evaluations: %d" % fcalls[0])
  
      result = OptimizeResult(
          fun=fval,
          nit=iterations,
          nfev=fcalls[0],
          status=warnflag,
          success=(warnflag == 0),
          message=msg,
          x=x,
      )
      if retall:
          result["allvecs"] = allvecs
      return result